diff --git a/gtsam.h b/gtsam.h
index 030a6ea01..b8aba73ff 100644
--- a/gtsam.h
+++ b/gtsam.h
@@ -370,6 +370,8 @@ class KalmanFilter {
 			const gtsam::SharedDiagonal& model);
 	gtsam::GaussianDensity* update(gtsam::GaussianDensity* p, Matrix H, Vector z,
 			const gtsam::SharedDiagonal& model);
+	gtsam::GaussianDensity* updateQ(gtsam::GaussianDensity* p, Matrix H, Vector z,
+	    Matrix Q);
 };
 
 //*************************************************************************
diff --git a/gtsam/linear/KalmanFilter.cpp b/gtsam/linear/KalmanFilter.cpp
index 224af162b..56a2692f3 100644
--- a/gtsam/linear/KalmanFilter.cpp
+++ b/gtsam/linear/KalmanFilter.cpp
@@ -145,7 +145,7 @@ namespace gtsam {
 	}
 
   /* ************************************************************************* */
-  KalmanFilter::State KalmanFilter::update(const State& p, const Matrix& H, const Vector& z,
+  KalmanFilter::State KalmanFilter::updateQ(const State& p, const Matrix& H, const Vector& z,
       const Matrix& Q) {
     Index k = step(p);
     Matrix M = inverse(Q), Ht = trans(H);
diff --git a/gtsam/linear/KalmanFilter.h b/gtsam/linear/KalmanFilter.h
index a5c21f117..bff512b95 100644
--- a/gtsam/linear/KalmanFilter.h
+++ b/gtsam/linear/KalmanFilter.h
@@ -143,7 +143,7 @@ namespace gtsam {
      *  physical property, such as velocity or acceleration, and G is derived from physics.
      *  This version allows more realistic models than a diagonal covariance matrix.
      */
-    State update(const State& p, const Matrix& H, const Vector& z,
+    State updateQ(const State& p, const Matrix& H, const Vector& z,
         const Matrix& Q);
 	};
 
diff --git a/gtsam/linear/tests/testKalmanFilter.cpp b/gtsam/linear/tests/testKalmanFilter.cpp
index f00ac8290..3fb8a91c6 100644
--- a/gtsam/linear/tests/testKalmanFilter.cpp
+++ b/gtsam/linear/tests/testKalmanFilter.cpp
@@ -272,8 +272,8 @@ TEST( KalmanFilter, QRvsCholesky ) {
 
 	// do the above update again, this time with a full Matrix Q
 	Matrix modelQ = diag(emul(sigmas,sigmas));
-  KalmanFilter::State pa3 = kfa.update(pa, H, z, modelQ);
-  KalmanFilter::State pb3 = kfb.update(pb, H, z, modelQ);
+  KalmanFilter::State pa3 = kfa.updateQ(pa, H, z, modelQ);
+  KalmanFilter::State pb3 = kfb.updateQ(pb, H, z, modelQ);
 
   // Check that they yield the same mean and information matrix
   EXPECT(assert_equal(pa3->mean(), pb3->mean()));